## MeloTTS Docker Deployment: Step-by-Step Tutorial
MeloTTS is one of the few TTS systems that runs efficiently on CPU — no GPU required. In this tutorial, I'll walk you through deploying MeloTTS with Docker, from pulling the image to generating your first speech file.
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## Prerequisites
- **Docker** and **Docker Compose** installed on your system
- At least **4GB RAM** available (2GB minimum)
- **2 CPU cores** recommended
- Basic familiarity with the command line
## Step 1: Pull the Docker Image
We'll use the community-maintained MeloTTS WebUI image that includes both the TTS engine and a clean web interface:
```bash
docker pull sensejworld/melotts:latest
```
This image bundles the MeloTTS Python library with a Gradio web UI, making it accessible through your browser without writing any code.
## Step 2: Run with Docker Compose
Create a `docker-compose.yml` file:
```yaml
services:
melotts:
image: sensejworld/melotts:latest
restart: unless-stopped
ports:
- 8080:8080
volumes:
- ./data/melotts:/data
```
Start the service:
```bash
docker compose up -d
```
## Step 3: Access the Web UI
Open your browser and navigate to:
```
http://localhost:8080
```
You'll see the Gradio interface with options to:
- Select language (EN, ES, FR, ZH, JP, KR)
- Enter text to synthesize
- Choose speaker/accent
- Download generated WAV files
## Step 4: Generate Your First Speech
1. Select **English** from the language dropdown
2. Enter your text: *"Hello! This is MeloTTS running in Docker. Speech synthesis has never been this easy."*
3. Click **Generate**
4. Listen to the output or download the WAV file
## Programmatic API Access
MeloTTS also exposes a REST API for programmatic access. Here's a Python example:
```python
import requests
response = requests.post(
'http://localhost:8080/synthesize',
json={
'text': 'Hello from the MeloTTS API!',
'language': 'EN',
'speaker': 'EN-US'
}
)
with open('output.wav', 'wb') as f:
f.write(response.content)
```
## Advanced: Voice Cloning
To clone a voice, provide a reference audio file:
```python
from melo.api import TTS
model = TTS(language='EN')
model.tts_to_file(
text="This is a cloned voice speaking.",
speaker_id='EN-US',
reference_audio='my_voice.wav',
output_path='cloned_output.wav'
)
```
For best results with voice cloning:
- Use 5-10 seconds of clean speech as reference
- Record in a quiet environment with minimal background noise
- Match the reference speaker's language to the target language
## Troubleshooting
### Port already in use
```bash
# Check what's using port 8080
sudo lsof -i :8080
# Use a different port in docker-compose.yml
ports:
- 8081:8080
```
### Out of memory errors
Reduce worker count or allocate more RAM to Docker:
```yaml
environment:
- MAX_WORKERS=1
```
### Slow generation on low-end CPUs
MeloTTS is already CPU-optimized, but you can reduce quality slightly for faster inference by using shorter input segments.
## Performance Notes
On a modern 4-core CPU (Intel i5 or equivalent):
- **Cold start**: ~30 seconds (model loading)
- **Per-sentence synthesis**: <1 second
- **RAM usage**: ~2GB steady state
- **Disk space**: ~1.5GB for Docker image + models
## Conclusion
MeloTTS in Docker is one of the fastest ways to get production-quality multilingual TTS running locally. The CPU-only inference means you can deploy it on affordable hardware without GPU costs, and the MIT license gives you full flexibility for commercial use.
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